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A general semiparametric hazards regression model: efficient estimation and structure selection
Author(s) -
Tong Xingwei,
Zhu Liang,
Leng Chenlei,
Leisenring Wendy,
Robison Leslie L.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5885
Subject(s) - covariate , semiparametric regression , proportional hazards model , semiparametric model , model selection , accelerated failure time model , econometrics , statistics , computer science , regression analysis , regression , mathematics , parametric statistics
We consider a general semiparametric hazards regression model that encompasses the Cox proportional hazards model and the accelerated failure time model for survival analysis. To overcome the nonexistence of the maximum likelihood, we derive a kernel‐smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency. In addition, we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model. The proposed method is able to estimate the model structure consistently and model parameters efficiently. Furthermore, variance estimation is straightforward. The proposed estimation performs well in simulation studies and is applied to the analysis of a real data set. Copyright © 2013 John Wiley & Sons, Ltd.